code: def test_byte_permutation(width_range: Tuple[int, int]) -> None:
      opts = argparse.Namespace()
      setattr(
          opts,
          "image_augmentation.random_uniform.width_range",
          width_range,
      )
  
      t = image_bytes.RandomUniformNoise(opts)
  
      size = 32
      samples = torch.randint(255, [size])
      x = {"samples": samples.clone()}
      out = t(x)["samples"]
      assert samples.shape == out.shape
  
      if width_range == (0, 0):
          assert torch.all(samples == out)
      else:
          assert torch.any(samples != out)
  
file_path: ml-cvnets/tests/transforms/test_image_bytes.py
imports: import argparse
  from typing import Tuple
  import pytest
  import torch
  from data.transforms import image_bytes
label_desc: Use image node_id 5 for calling a global function with example usage: test_byte_permutation(width_range) without return types